How far do LLMs give us a step change in how good a search and recommendation system can be? Do they let you build one without needing a vast user base of your own?

With every platform shift, we want to measure the growth but we’re confused about what to measure. That’s partly a problem of data and definitions, but it’s really a question about what this is going to be.

Generative AI chatbots might be a life-changing transformation in the nature of computing, that can replace all software, but so far, most of its users only pick it up every week or two, and far fewer have made it part of their lives. Is that a time problem or a product problem?

We’re proud to back Manas AI to build the future of drug discovery

Why you need to rethink your pricing for the agentic age

We're excited to announce that Parloa has raised a $120M Series C, at a $1B valuation.

Software ate the world. Uber and Airbnb didn’t sell software - they disrupted and redefined markets. But what kind of disruption are we talking about ?

It matters that Apple’s new Siri will be late, and it matters more that Apple didn’t realise. Is it more than that?

Every week there’s a better AI model that gives better answers. But a lot of questions don’t have better answers, only ‘right’ answers, and these models can’t do that. So what does ‘better’ mean, how do we manage these things, and should we change what we expect from computers?

OpenAI’s Deep Research is built for me, and I can’t use it. It’s another amazing demo, until it breaks. But it breaks in really interesting ways.

We’re excited to announce that we have promoted Chandar Lal to join our partnership.

Every year, I produce a big presentation exploring macro and strategic trends in the tech industry. For 2025, "AI eats the world"

Will Apple build a search engine? Will ChatGPT change search? Does it matter?

Hundreds of millions of people have tried ChatGPT, but most of them haven’t been back. Every big company has done a pilot, but far fewer are in deployment. Some of this is just a matter of time. But LLMs might also be a trap: they look like products and they look magic, but they aren’t. Maybe we have to go through the slow, boring hunt for product-market fit after all.

Meta has spent at least $50bn on VR and AR so far, but we’re still in the VR winter: the devices aren’t good enough or cheap enough and the user base is flat. But no matter how good the devices get, how many people will care?

Backing the next generation of AI-powered video security

Apple has showed a bunch of cool ideas for generative AI, but much more, it is pointing to most of the big questions and proposing a different answer - that LLMs are commodity infrastructure, not platforms or products.

How do we build mass-market products that change the world around a technology that gets things ‘wrong’? What does wrong mean, and how is that useful?

How do we think about a fundamentally unknown and unknowable risk, when the experts agree only that they have no idea?